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Towards a RISC Framework for Efficient Contextualisation in the IoT

Lookup NU author(s): Professor Raj Ranjan


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© 2017 IEEE. The Internet of Things (IoT) is a new internet evolution that involves connecting billions of internet-connected devices that we refer to as IoT things. These devices can communicate directly and intelligently over the Internet, and generate a massive amount of data that needs to be consumed by a variety of IoT applications. This paper focuses on the automatic contextualisation of IoT data, which also involves distilling information and knowledge from the IoT aiming to simplify answering the following fundamental questions that often arises in IoT applications: Which data collected by IoT are relevant to myself and the IoT Things I care for? Related work around context management and contextualisation ranges from database techniques that involve query re-writing, to semantic web and rule-based context management approaches, to machine learning and data science-based solutions in mobile and ambient computing. All such existing approaches have two main aspects in common: They are highly incompatible and horribly inefficient from a scalability and performance perspective. In this paper, we discuss a new RISC Contextualisation Framework (RCF) we have developed, implemented key aspects of, and assess its scalability. RCF provides fundamental contextualisation concepts that can be mapped to all existing contextualisation approaches for IoT data (and in this sense, it provides a common denominator that unifies the contextualisation space). RCF can be easily implemented as a cloud-based service, and provides better scalability and performance that any of the existing content management and contextualisation approaches in the IoT space.

Publication metadata

Author(s): Georgakopoulos D, Yavari A, Jayaraman PP, Ranjan R

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)

Year of Conference: 2017

Pages: 1993-1996

Online publication date: 17/07/2017

Acceptance date: 02/04/2016

ISSN: 1063-6927

Publisher: IEEE


DOI: 10.1109/ICDCS.2017.308

Library holdings: Search Newcastle University Library for this item

ISBN: 9781538617915